Biology subjects

pan, x.

Publications and source records attributed to pan, x..

3 recordsLinked to original sources

bioRxiv2023

Abnormal homeostasis of P53 gene knockout mice can be reflected in urinary proteome

In this experiment, the urinary proteome analysis of p53 knockout mice at four relatively early time points before death was performed from three dimensions. One was to compare the differential proteins screened by homozygote and heterozygote at the same time point; the other was to compare the differential proteins screened by homozygote and wild-type as well as heterozygote and wild-type at the same time point; the third was to compare the differential proteins screened by homozygote and heterozygote before and after each time point, and to screen the differential proteins of three mice by self-control analysis and screening. Later, enrichment analysis of the differential proteins was performed mainly by Metascape and String, and disease association analysis of gene or variant genotype-phenotype with the help of DisGeNET and Monarch. We found that the biological pathways mainly related to metabolism were most abundant, and at the same time abnormal homeostasis, cancer and tumor, cardiovascular diseases, and neurodegenerative diseases appeared. However, under the analysis of Monarch, homozygotes or heterozygotes contained abnormal homeostasis at all time points, and most of them ranked the first or at least the top, while all timepoints in the growth and development of each normal rat were not enriched to abnormal homeostasis and other disease pathways.

biochemistry↗

bioRxiv2023

Structural insights into ligand recognition and selectivity of the human hydroxycarboxylic acid receptor HCAR2

Hydroxycarboxylic acid receptor 2 (HCAR2) belongs to the family of class A G-protein-coupled receptors with key roles in regulating lipolysis and free fatty acid formation in humans. It is deeply involved in many pathophysiological processes and serves as an attractive target for the treatment of neoplastic, autoimmune, neurodegenerative, inflammatory, and metabolic diseases. Here, we report four cryo-EM structures of human HCAR2-Gi1 complexes with or without agonists, including the drugs niacin and acipimox, and the highly subtype-specific agonist MK-6892. Combined with molecular docking and functional analysis, we have revealed the recognition mechanism of HCAR2 for different agonists and summarized the general pharmacophore features of HCAR2 agonists, which are based on three key residues R1113.36, S17945.52, and Y2847.43. Notably, the MK-6892-HCAR2 structure shows an extended binding pocket relative to other agonist-bound HCAR2 complexes. In addition, the key residues that determine the ligand selectivity between the HCAR2 and HCAR3 are also illuminated. Our findings provide structural insights into the ligand recognition, selectivity, activation, and G protein coupling mechanism of HCAR2, which sheds light on the design of new HCAR2-targeting drugs for greater efficacy, higher selectivity, and fewer or no side effects.

cell biology↗

bioRxiv2020

A peptide bond based energy score improve identification of the native structures

Protein structure resolution has lagged far behind sequence determination, as it is often laborious and time-consuming to resolve individual protein structure - more often than not even impossible. For computational prediction, due to the lack of detailed knowledge on the folding driving forces, how to design an energy function is still an open question. Furthermore, an effective criterion to evaluate the performance of the energy function is also lacking. Here we present a novel knowledge-based-energy scoring function, simply considering the interactions of peptide bonds, rather than, as conventionally, the residues or atoms as the most important energy contribution. This energy scoring was evaluated by selecting the X-ray structure from a large number of possibilities. It not only outperforms the best of the previously published statistical potentials, but also has very low computational expense. Besides, we suggest an alternative criterion to evaluate the performance of the energy scoring function, measured by the template modeling score of the selected rank-one. We argue that the comparison should allow for some deviation between the x-ray and predicted structures. Collectively, this accurate and simple energy scoring function, together with the optimized criterion, will significantly advance the computational protein structure prediction.

bioinformatics↗